DESCRIPTION (Applicant's abstract): Methodologies to characterize cognition in the elderly using secondary and administrative data sets lag behind those for measures assessing physical function. However, because of the inter-relatedness of physically active life expectancy (P-ALE) and cognitively-intact life expectancy (C-ALE), it is difficult to investigate mechanisms contributing to differential total active life expectancy (T-ALE) without having improved measures for C-ALE. Assessing cognitive performance using secondary data presents challenges that are unparalleled in assessments of physical limitations. In this study, we will define and validate measures for C-ALE. To do this, we will employ two population-based data sets, both funded by the National Institute on Aging (NIA), one to develop cognitive measures suitable for use in analyses of secondary data and one to validate them. The first population, consisting of 10,887 community-dwelling participants from the 1984, 1989, or 1994 National Long Term Care Surveys linked to their respective Medicare claims, will be used to develop measures. Initial measures will focus on three categories of survey questions that may contain direct or indirect information on cognitive status: Rank reports of dementia, "senility," or Alzheimer's disease; performance on cognitive screening tests; and other questions that contain memory-related information. Using Medicare ICD-9-CM codes on the NLTCS population, we will create Medicare-derived variables that maintain clinical relevance and variables with collapsed categories suitable for examining correspondence with less clinically-detailed NLTCS survey data. The second population, consisting of data on approximately 2,200 original participants and 2,000 new enrollees of the Washington Heights Inwood Columbia Aging Project (WHICAP), will be used to validate the cognitive measures developed on the NLTCS population, with and without their respective Medicare diagnoses. WHICAP is a longitudinal, community-based, multiethnic study of dementia and aging with annual follow-up, including a battery of neuropsychological tests; medical examinations for those with abnormal cognition; laboratory, MRI, and autopsy data. We will develop multivariate models for predicting baseline cognitive status, C-ALE, and cognitive decline during follow-up investigating demographic, socioeconomic, function, comorbid conditions, and disease risk variables. Our validated measures of dementia and cognition will be used to assess the relation between baseline cognitive function, C-ALE, and subsequent patterns of health care use and expenditures. We will report findings that could be used to inform the design and data collection of cognitively-related variables in future prospectively conducted surveys.